Conventionally, sensor data management apparatuses have been known that store and manage time-series data (sensor data) transmitted from a plurality of sensors. The sensor data is characterized in that the data is registered in chronological order and that, in most cases, a periodicity is observed in clock times when the data is registered. Conventional sensor data management apparatuses of the relational database management system (RDBMS) type in most cases each store and manage pieces of sensor data acquired from a plurality of sensors in a common table while treating the respective pieces of sensor data as records for which the IDs of the respective sensors and clock times are used as keys. This technique is, however, disadvantageous in that it involves complicated management because data increases for storing the sensor IDs and because there is no guarantee that the pieces of sensor data are registered in chronological order.
To resolve this disadvantage, sensor data management apparatuses in some cases employ a technique such that sensor data is managed on a sensor-to-sensor basis. In comparison with the technique of using a common table to manage sensor data acquired from a plurality of sensors, this technique is advantageous because it involves a smaller amount of data. This technique enables efficient search because pieces of sensor data are lined up in groups in chronological order.
In recent years, sensor apparatuses that are connected to networks have been increasingly common as words such as IoT (Internet of things) and M2M (Machine-to-Machine) represent. When a massive amount of sensor data of various kinds is managed, a technique in which the data is managed on a sensor-to-sensor basis involves a risk of decreasing the processing speed. That is, in such a technique, sensor data is stored after data areas are secured for corresponding sensors, which means that the sensor data needs to be read out from a file stored in, for example, a hard disk drive (HDD) into a buffer on a memory and be written into a file from the buffer on a sensor-to-sensor basis. Consequently, when sensor data of multiple different sensors are successively accessed, the sensor data that are accessed is less likely on the buffer, and a large amount of read and write processing is incurred to the extent that decreases the processing speed.
To overcome this inconvenience, an attempt has been made to decrease the processing speed by dividing multiple sensors into groups and preparing two databases that are one configured to manage sensor data on a group-to-group basis and the other configured to manage sensor data on a sensor-to-sensor basis. For example, the Specification of US Patent Published Application No. 2014/0122022 discloses a sensor data management apparatus configured to: store sensor data in a first database on a group-to-group basis when the sensor data is registered; and, when a certain amount of data has accumulated in the first database, transfer the sensor data stored in the first database to second databases prepared for the respective sensors.
This technique allows for reading and writing data between a buffer and each file on a group-to-group basis. Consequently, if multiple sensors can be grouped in a manner that allows the same group to consist of sensors that register sensor data at clock times close to one another, a high buffer hit rate can be achieved. When a large number of sensors are registered by various applications, however, this technique has difficulty appropriately grouping multiple sensors based on information provided by the applications beforehand; more specifically, this technique can appropriately group the sensors with respect to each application but has difficulty grouping the sensors across the applications, for example. When a plural pieces of data are managed using one table, schemas of the managed sensor data need to be the same, and kinds of sensors that can be put into a group are therefore limited. Furthermore, one piece of sensor data is managed using a plurality of databases, which makes it necessary to establish a storage process and a search process for each of the two databases having different formats, resulting in a complicated mechanism.
As described above, conventional sensor data management apparatuses have disadvantages due to the techniques employed therein, and need to be improved for higher efficiency in storage and management of sensor data.